The Effects of a Megafire on Ecosystem Services and the Pace of Landscape Recovery

(1) Background: Megafires have affected several regions in the world (e.g., Australia, California), including, in 2017, the central and south-central zones of Chile. These areas represent real laboratories to monitor the impacts on the sustainability of landscapes and their recovery after fires. The present research examines the modification of dynamics and the provision of ecosystem services by a megafire in a Mediterranean landscape in central Chile, combining remote sensing technologies and ecosystem service assessments. (2) Methods: Land cover and spectral indices (NBRI, BAIS-2, NDVI, and EVI) were measured using Sentinel-2 imagery, while the provision of ecosystem services was evaluated using an expert-based matrix. (3) Results: The megafire affected forest plantations, formerly the dominant land cover, as well as other ecosystems, e.g., native forests. After five years, the landscape is dominated by exotic shrublands and grasslands. (4) Conclusions: The megafire caused a loss of 50% of the landscape’s capacity to supply ecosystem services. Given that native forests are the best provider of ecosystem services in this landscape, restoration is a key to recovering landscape sustainability.

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The Impacts of Native Forests and Forest Plantations on Water Supply in Chile

Forests ◽

10.3390/f10060473 ◽

2019 ◽

Vol 10 (6) ◽

pp. 473 ◽

Cited By ~ 6

Author(s):

Camila Alvarez-Garreton ◽

Antonio Lara ◽

Juan Pablo Boisier ◽

Mauricio Galleguillos

Keyword(s):

Decision Making ◽

Land Cover ◽

Water Supply ◽

Annual Runoff ◽

Mitigation Strategies ◽

Land Cover Changes ◽

Forest Plantations ◽

Central Chile ◽

South Central ◽

Native Forests

Over the past 40 years, south-central Chile has experienced important land-use-induced land cover changes, with massive conversion from native forests (NF) to Pinus radiata D.Don and Eucalyptus spp. exotic forest plantations (FP). Several case studies have related this conversion to a reduction in water supply within small catchments (<100 ha). In this work, we explore the impacts of NF and FP on streamflow by using a large-sample catchment dataset recently developed for Chile. We select 25 large forested catchments (>20,000 ha) in south-central Chile (35° S–41° S), analyze their land cover and precipitation spatial distributions, and fit a regression model to quantify the influence of NF, FP, grassland (GRA) and shrubland (SHR) partitions on annual runoff. To assess potential effects of land cover changes on water supply, we use the fitted model (R2 = 0.84) in synthetic experiments where NF, GRA and SHR covers within the catchments are replaced by patches of FP. We show that annual runoff consistently decreases with increments of FP, although the magnitude of the change (ranging from 2.2% to 7.2% mean annual runoff decrease for 10,000 ha increment in FP) depends on several factors, including the initial land cover partition within the basin, the replaced land cover class, the area of the catchment, and the type of catchment (drier or humid). Finally, in the context of the mitigation strategies pledged in the Chilean NDC (Nationally Determined Contributions defined after the Paris Agreement), which include the afforestation of 100,000 ha (mainly native forest) by 2030, we quantify the impacts on water supply due to the afforestation of 100,000 ha with different combinations of NF and FP. We show that annual runoff is highly sensitive to the relative area of FP to NF: ratios of FP to NF areas of 10%, 50% and 90% would lead to 3%, −18% and −40% changes in mean annual runoff, respectively. Our results can be used in the discussion of public policies and decision-making involving forests and land cover changes, as they provide scientifically-based tools to quantify expected impacts on water resources. In particular, this knowledge is relevant for decision making regarding mitigation strategies pledged in the Chilean NDC.

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Spatial Pattern Analysis of the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area Using Sentinel-1 and Sentinel-2 Imagery Based on Deep Learning Method

Sustainability ◽

10.3390/su13137044 ◽

2021 ◽

Vol 13 (13) ◽

pp. 7044

Author(s):

Dawei Wen ◽

Song Ma ◽

Anlu Zhang ◽

Xinli Ke

Keyword(s):

Hong Kong ◽

Deep Learning ◽

Ecosystem Services ◽

Land Cover ◽

Spatial Pattern ◽

Sustainable Urban Development ◽

Spatial Pattern Analysis ◽

Bay Area ◽

Urban Rural ◽

Sentinel 2

Assessment of ecosystem services supply, demand, and budgets can help to achieve sustainable urban development. The Guangdong-Hong Kong-Macao Greater Bay Area, as one of the most developed megacities in China, sets up a goal of high-quality development while fostering ecosystem services. Therefore, assessing the ecosystem services in this study area is very important to guide further development. However, the spatial pattern of ecosystem services, especially at local scales, is not well understood. Using the available 2017 land cover product, Sentinel-1 SAR and Sentinel-2 optical images, a deep learning land cover mapping framework integrating deep change vector analysis and the ResUnet model was proposed. Based on the produced 10 m land cover map for the year 2020, recent spatial patterns of the ecosystem services at different scales (i.e., the GBA, 11 cities, urban–rural gradient, and pixel) were analyzed. The results showed that: (1) Forest was the primary land cover in Guangzhou, Huizhou, Shenzhen, Zhuhai, Jiangmen, Zhaoqing, and Hong Kong, and an impervious surface was the main land cover in the other four cities. (2) Although ecosystem services in the GBA were sufficient to meet their demand, there was undersupply for all the three general services in Macao and for the provision services in Zhongshan, Dongguan, Shenzhen, and Foshan. (3) Along the urban–rural gradient in the GBA, supply and demand capacity showed an increasing and decreasing trend, respectively. As for the city-level analysis, Huizhou and Zhuhai showed a fluctuation pattern while Jiangmen, Zhaoqing, and Hong Kong presented a decreasing pattern along the gradient. (4) Inclusion of neighborhood landscape led to increased demand scores in a small proportion of impervious areas and oversupply for a very large percent of bare land.

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GIS-based site suitability analysis and ecosystem services approach for supporting renewable energy development in south-central Chile

Renewable Energy ◽

10.1016/j.renene.2021.10.008 ◽

2021 ◽

Author(s):

Yenisleidy Martínez-Martínez ◽

Jo Dewulf ◽

Yannay Casas-Ledón

Keyword(s):

Renewable Energy ◽

Ecosystem Services ◽

Energy Development ◽

Suitability Analysis ◽

Site Suitability ◽

Central Chile ◽

South Central ◽

Renewable Energy Development

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Exploring the utility of Sentinel-2 MSI derived spectral indices in mapping burned areas in different land-cover types

Scientific African ◽

10.1016/j.sciaf.2020.e00565 ◽

2020 ◽

Vol 10 ◽

pp. e00565

Author(s):

Kudzai Shaun Mpakairi ◽

Henry Ndaimani ◽

Blessing Kavhu

Keyword(s):

Land Cover ◽

Spectral Indices ◽

Land Cover Types ◽

Burned Areas ◽

Sentinel 2

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Assessing the socio-economic and land-cover drivers of wildfire activity and its spatiotemporal distribution in south-central Chile

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.152002 ◽

2021 ◽

pp. 152002

Author(s):

Rocío A. Pozo ◽

Mauricio Galleguillos ◽

Mauro E. González ◽

Felipe Vásquez ◽

Rodrigo Arriagada

Keyword(s):

Land Cover ◽

Spatiotemporal Distribution ◽

Central Chile ◽

South Central

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Land-cover change to forest plantations: Proximate causes and implications for the landscape in south-central Chile

Landscape and Urban Planning ◽

10.1016/j.landurbplan.2012.04.006 ◽

2012 ◽

Vol 107 (1) ◽

pp. 12-20 ◽

Cited By ~ 72

Author(s):

Laura Nahuelhual ◽

Alejandra Carmona ◽

Antonio Lara ◽

Cristian Echeverría ◽

Mauro E. González

Keyword(s):

Land Cover ◽

Land Cover Change ◽

Forest Plantations ◽

Central Chile ◽

South Central ◽

Proximate Causes

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Sentinel-1 and Sentinel-2 Data for Savannah Land Cover Mapping: Optimising the Combination of Sensors and Seasons

Remote Sensing ◽

10.3390/rs12233862 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3862

Author(s):

Joana Borges ◽

Thomas P. Higginbottom ◽

Elias Symeonakis ◽

Martin Jones

Keyword(s):

Ecosystem Services ◽

Land Cover ◽

Dry Season ◽

Sensor Data ◽

Land Cover Mapping ◽

Heterogeneous Environments ◽

Wet Season ◽

Conservation Area ◽

Free Data ◽

Sentinel 2

Savannahs are heterogeneous environments with an important role in supporting biodiversity and providing essential ecosystem services. Due to extensive land use/cover changes and subsequent land degradation, the provision of ecosystems services from savannahs has increasingly declined over recent years. Mapping the extent and the composition of savannah environments is challenging but essential in order to improve monitoring capabilities, prevent biodiversity loss and ensure the provision of ecosystem services. Here, we tested combinations of Sentinel-1 and Sentinel-2 data from three different seasons to optimise land cover mapping, focusing in the Ngorongoro Conservation Area (NCA) in Tanzania. The NCA has a bimodal rainfall pattern and is composed of a combination savannah and woodland landscapes. The best performing model achieved an overall accuracy of 86.3 ± 1.5% and included a combination of Sentinel-1 and 2 from the dry and short-dry seasons. Our results show that the optical models outperform their radar counterparts, the combination of multisensor data improves the overall accuracy in all scenarios and this is particularly advantageous in single-season models. Regarding the effect of season, models that included the short-dry season outperform the dry and wet season models, as this season is able to provide cloud free data and is wet enough to allow for the distinction between woody and herbaceous vegetation. Additionally, the combination of more than one season is beneficial for the classification, specifically if it includes the dry or the short-dry season. Combining several seasons is, overall, more beneficial for single-sensor data; however, the accuracies varied with land cover. In summary, the combination of several seasons and sensors provides a more accurate classification, but the target vegetation types should be taken into consideration.

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